• Korean Journal of Construction Engineering and Management
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• v.20 no.4
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• pp.22-33
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• 2019

The government has steadily revised and developed laws and systems for activating remodeling of apartments in response to the problems of aged apartments. However, despite such efforts, remodeling has yet to be activated. For many reasons, this study noted that there were no tools for reasonable profitability judgements and decision making in the preliminary stages of the remodeling project. Thus, the feasibility evaluation model was developed. Generally, the profitability judgements are made after the conceptual design. However, decisions to drive remodeling projects are made at the preliminary stage. So a feasibility evaluation model is required at the preliminary stage. Accordingly, In this study, a feasibility evaluation model was developed for determining preliminary stage profitability. Construction costs, business expenses, financial expenses, and generally sales revenue were calculated using the initial available information and remodeling variables derived through the existing cases. Through this process, we developed an algorithm that can give an overview of the return on investment. In addition, the preliminary stage feasibility evaluation model developed was applied to three cases to verify the applicability of the model. Although applied in three cases, the difference between the model's forecast and actual case values is less than 5%, which is considered highly applicable. If cases are expanded in the future, it will be a useful tool that can be used in actual work. The feasibility evaluation model developed in this study will support decision making by union members, and if the model is applied in different regions, it will be expected to help local governments to understand the size of possible remodeling projects.

• Knowledge Management Research
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• v.22 no.4
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• pp.71-85
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• 2021

The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.114-129
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• 2023

In the era of the 4th industrial revolution, the importance of the 4th industrial revolution technology is increasing in the service industry. The purpose of this study is to identify the development and utilization status of the 4th industrial revolution technology in the service industry and to derive the characteristics and implications of the 4th industrial revolution technology innovation in the service industry. In this study, research and analysis were conducted based on the business activity survey data in order to identify the technological innovation characteristics of the 4th industrial revolution in the service industry. The 4th industrial revolution technology in the service industry was analyzed in terms of company ratio, technology development and utilization rate, development/utilization technology, technology application field, and technology development method. In addition, the trend of the 4th industrial revolution technology change in the service industry was also analyzed. The 4th industrial revolution technology utilization and development status of other industries was compared and analyzed. In particular, the service industry 4th industrial revolution technology innovation type was divided into 4 types from the perspective of the 4th industrial revolution company ratio and the 4th industrial revolution company ratio growth rate, and types for each service industry were derived. The characteristics and implications of the 4th industrial revolution technology innovation in the service industry were presented from nine perspectives. As a result of the study, it was found that companies in the service industry were developing or using 4th industrial revolution technologies more actively than companies in other industries, and it was analyzed that the gap was further widening. By service industry, information and communication, finance and insurance, and educational service showed relatively high rates of developing or utilizing 4th industrial revolution technologies. The service industries in which the share of 4th industrial revolution companies increased the most were real estate, education service, health and social welfare service. In particular, cloud, big data, and artificial intelligence were analyzed as the three core technologies of the fourth industrial revolution. The service industry can be classified into 4 types in terms of the 4th industrial revolution company ratio and growth rate, and service industry innovation measures that reflect the differentiated innovation characteristics of each type are needed.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• The Journal of the Korean life insurance medical association
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• v.24
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• pp.49-78
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• 2005

Ⅰ. 연구(硏究) 배경(背景) 및 목적(目的) o 우리나라 보험시장의 세대가입율은 86%로 보험시장 성숙기에 진입하였으며 기존의 전통적인 전업채널에서 방카슈랑스의 도입, 온라인전문보험사의 출현, TM 영업의 성장세 等멀티채널로 진행되고 있음 o LTC(장기간병), CI(치명적질환), 실손의료보험 등(等)선 진형 건강상품의 잇따른 출시로 보험리스크 관리측면에서 언더라이팅의 대비가 절실한 시점임 o 상품과 마케팅 等언더라이팅 측면에서 매우 밀접한 영역의 변화에 발맞추어 언더라이팅의 인수기법의 선진화가 시급히 요구되는 상황하에서 위험을 적절히 분류하고 평가하는 선진적 언더라이팅 기법 구축이 필수 적임 o 궁극적으로 고객의 다양한 보장니드 충족과 상품, 마케팅, 언더라이팅의 경쟁력 강화를 통한 보험사의 종합이익 극대화에 기여할 수 있는 방안을 모색하고자 함 Ⅱ. 선진보험시장(先進保險市場)Risk 세분화사례(細分化事例) 1. 환경적위험(環境的危險)에 따른 보험료(保險料) 차등(差等) (1) 위험직업 보험료 할증 o 미국, 유럽등(等) 대부분의 선진시장에서는 가입당시 피보험자의 직업위험도에 따라 보험료를 차등 적용중(中)임 o 가입하는 보장급부에 따라 직업 분류방법 및 할증방식도 상이하며 일반사망과 재해사망,납입면제, DI에 대해서 별도의 방법을 사용함 o 할증적용은 표준위험율의 일정배수를 적용하여 할증 보험료를 산출하거나, 가입금액당 일정한 추가보험료를 적용하고 있음 - 광부의 경우 재해사망 가입시 표준위험율의 300% 적용하며, 일반사망 가입시 $1,000당 $2.95 할증보험료 부가 (2) 위험취미 보험료 할증 o 취미와 관련 사고의 지속적 다발로 취미활동도 위험요소로 인식되어 보험료를 차등 적용중(中)임 o 할증보험료는 보험가입금액당 일정비율로 부가(가입 금액과 무관)하며, 신종레포츠 등(等)일부 위험취미는 통계의 부족으로 언더라이터가 할증율 결정하여 적용함 - 패러글라이딩 년(年)$26{\sim}50$회(回) 취미생활의 경우 가입금액 $1,000당 재해사망 $2, DI보험 8$ 할증보험료 부가 o 보험료 할증과는 별도로 위험취미에 대한 부담보를 적용함. 위험취미 활동으로 인한 보험사고 발생시 사망을 포함한 모든 급부에 대한 보장을 부(不)담보로 인수함. (3) 위험지역 거주/ 여행 보험료 할증 o 피보험자가 거주하고 있는 특정국가의 임시 혹은 영구적 거주시 기후위험, 거주지역의 위생과 의료수준, 여행위험, 전쟁과 폭동위험 등(等)을 고려하여 평가 o 일반사망, 재해사망 등(等)보장급부별로 할증보험료 부가 또는 거절 o 할증보험료는 보험全기간에 대해 동일하게 적용 - 러시아의 경우 가입금액 $1,000당 일반사망은 2$의 할증보험료 부가, 재해사망은 거절 (4) 기타 위험도에 대한 보험료 차등 o 비행관련 위험은 세가지로 분류(항공운송기, 개인비행, 군사비행), 청약서, 추가질문서, 진단서, 비행이력 정보를 바탕으로 할증보험료를 부가함 - 농약살포비행기조종사의 경우 가입금액 $1,000당 일반사망 6$의 할증보험료 부가, 재해사망은 거절 o 미국, 일본등(等)서는 교통사고나 교통위반 관련 기록을 활용하여 무(無)사고운전자에 대해 보험료 할인(우량체 위험요소로 활용) 2. 신체적위험도(身體的危險度)에 따른 보험료차등(保險料差等) (1) 표준미달체 보험료 할증 1) 총위험지수 500(초과위험지수 400)까지 인수 o 300이하는 25점단위, 300점 초과는 50점 단위로 13단계로 구분하여 할증보험료를 적용중(中)임 2) 삭감법과 할증법을 동시 적용 o 보험금 삭감부분만큼 할증보험료가 감소하는 효과가 있어 청약자에게 선택의 기회를 제공할수 있으며 고(高)위험 피보험자에게 유용함 3) 특정암에 대한 기왕력자에 대해 단기(Temporary)할증 적용 o 질병성향에 따라 가입후 $1{\sim}5$년간 할증보험료를 부가하고 보험료 할증 기간이 경과한 후에는 표준체보험료를 부가함 4) 할증보험료 반환옵션(Return of the extra premium)의 적용 o 보험계약이 유지중(中)이며, 일정기간 생존시 할증보험료가 반환됨 (2) 표준미달체 급부증액(Enhanced annuity) o 영국에서는 표준미달체를 대상으로 연금급부를 증가시킨 증액형 연금(Enhanced annuity) 상품을 개발 판매중(中)임 o 흡연, 직업, 병력 등(等)다양한 신체적, 환경적 위험도에 따라 표준체에 비해 증액연금을 차등 지급함 (3) 우량 피보험체 가격 세분화 o 미국시장에서는 $8{\sim}14$개 의적, 비(非)의적 위험요소에 대한 평가기준에 따라 표준체를 최대 8개 Class로 분류하여 할인보험료를 차등 적용 - 기왕력, 혈압, 가족력, 흡연, BMI, 콜레스테롤, 운전, 위험취미, 거주지, 비행력, 음주/마약 등(等) o 할인율은 회사, Class, 가입기준에 따라 상이(최대75%)하며, 가입연령은 최저 $16{\sim}20$세, 최대 $65{\sim}75$세, 최저보험금액은 10만달러(HIV검사가 필요한 최저 금액) o 일본시장에서는 $3{\sim}4$개 위험요소에 따라 $3{\sim}4$개 Class로 분류 우량체 할인중(中)임 o 유럽시장에서는 영국 등(等)일부시장에서만 비(非)흡연할인 또는 우량체할인 적용 Ⅲ. 국내보험시장(國內保險市場) 현황(現況)및 문제점(問題點) 1. 환경적위험도(環境的危險度)에 따른 가입한도제한(加入限度制限) (1) 위험직업 보험가입 제한 o 업계공동의 직업별 표준위험등급에 따라 각 보험사 자체적으로 위험등급별 가입한도를 설정 운영중(中)임. 비(非)위험직과의 형평성, 고(高)위험직업 보장 한계, 수익구조 불안정화 등(等)문제점을 내포하고 있음 - 광부의 경우 위험1급 적용으로 사망 최대 1억(億), 입원 1일(日) 2만원까지 제한 o 금융감독원이 2002년(年)7월(月)위험등급별 위험지수를 참조 위험율로 인가하였으나, 비위험직은 70%, 위험직은 200% 수준으로 산정되어 현실적 적용이 어려움 (2) 위험취미 보험가입 제한 o 해당취미의 직업종사자에 준(準)하여 직업위험등급을 적용하여 가입 한도를 제한하고 있음. 추가질문서를 활용하여 자격증 유무, 동호회 가입등(等)에 대한 세부정보를 입수하지 않음 - 패러글라이딩의 경우 위험2급을 적용, 사망보장 최대 2 억(億)까지 제한 (3) 거주지역/ 해외여행 보험가입 제한 o 각(各)보험사별로 지역적 특성상 사고재해 다발 지역에 대해 보험가입을 제한하고 있음 - 강원, 충청 일부지역 상해보험 가입불가 - 전북, 태백 일부지역 입원급여금 1일(日)2만원이내 o 해외여행을 포함한 해외체류에 대해서는 일정한 가입 요건을 정하여 운영중(中)이며, 가입한도 설정 보험가입을 제한하거나 재해집중보장 상품에 대해 거절함 - 러시아의 경우 단기체류는 위험1급 및 상해보험 가입 불가, 장기 체류는 거절처리함 2. 신체적위험도(身體的危險度)에 따른 인수차별화(引受差別化) (1) 표준미달체 인수방법 o 체증성, 항상성 위험에 대한 초과위험지수를 보험금삭감법으로 전환 사망보험에 적용(최대 5년(年))하여 5년(年)이후 보험 Risk노출 심각 o 보험료 할증은 일부 회사에서 주(主)보험 중심으로 사용중(中)이며, 총위험지수 300(8단계)까지 인수 - 주(主)보험 할증시 특약은 가입 불가하며, 암 기왕력자는 대부분 거절 o 신체부위 39가지, 질병 5가지에 대해 부담보 적용(입원, 수술 등(等)생존급부에 부담보) (2) 비(非)흡연/ 우량체 보험료 할인 o 1999년(年)최초 도입 이래 $3{\sim}4$개의 위험요소로 1개 Class 운영중(中)임 S생보사의 경우 비(非)흡연우량체, 비(非)흡연표준체의 2개 Class 운영 o 보험료 할인율은 회사, 상품에 따라 상이하며 최대 22%(영업보험료기준)임. 흡연여부는 뇨스틱을 활용 코티닌테스트를 실시함 o 우량체 판매는 신계약의 $2{\sim}15%$수준(회사의 정책에 따라 상이) Ⅳ. 언더라이팅 기법(技法) 선진화(先進化) 방안(方案) 1. 직업위험도별 보험료 차등 적용 o 생 손보 직업위험등급 일원화와 연계하여 3개등급으로 위험지수개편, 비위험직 기준으로 보험요율 차별적용 2. 위험취미에 대한 부담보 적용 o 해당취미를 원인으로 보험사고(사망포함) 발생시 부담보 제도 도입 3. 표준미달체 인수기법 선진화를 통한 인수범위 대폭 확대 o 보험료 할증법 적용 확대를 통한 Risk 헷지로 총위험지수 $300{\rightarrow}500$으로 확대(거절건 최소화) 4. 보험료 할증법 보험금 삭감 병행 적용 o 삭감기간을 적용한 보험료 할증방식 개발, 고객에게 선택권 제공 5. 기한부 보험료할증 부가 o 위암, 갑상선암 등(等)특정암의 성향에 따라 위험도가 높은 가입초기에 평준할증보험료를 적용하여 인수 6. 보험료 할증법 부가특약 확대 적용, 부담보 병행 사용 o 정기특약 등(等)사망관련 특약에 할증법 확대, 생존급부 특약은 부담보 7. 표준체 고객 세분화 확대 o 콜레스테롤, HDL 등(等)위험평가요소 확대를 통한 Class 세분화 Ⅴ. 기대효과(期待效果) 1. 고(高)위험직종사자, 위험취미자, 표준미달체에 대한 보험가입 문호개방 2. 보험계약자간 형평성 제고 및 다양한 고객의 보장니드에 부응 3. 상품판매 확대 및 Risk헷지를 통한 수입보험료 증대 및 사차익 개선 4. 본격적인 가격경쟁에 대비한 보험사 체질 개선 5. 회사 이미지 제고 및 진단 거부감 해소, 포트폴리오 약화 방지 Ⅵ. 결론(結論) o 종래의 소극적이고 일률적인 인수기법에서 탈피하여 피보험자를 다양한 측면에서 위험평가하여 적정 보험료 부가와 합리적 가입조건을 제시하는 적절한 위험평가 수단을 도입하고, o 언더라이팅 인수기법의 선진화와 함께 언더라이팅 인력의 전문화, 정보입수 및 시스템 인프라의 구축 등이 병행함으로써, o 보험사의 사차손익 관리측면에서 뿐만 아니라 보험시장 개방 및 급변하는 보험환경에 대비한 한국 생보언더라이팅 경쟁력 강화 및 언더라이터의 글로벌화에도 크게 기여할 것임.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Journal of Korean Society of Forest Science
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• v.15 no.1
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• pp.1-38
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• 1972

1. Objective of the Study The objective of the study was to make fundamental suggestions for drawing a forest insurance system applicable in Korea by investigating forest insurance systems undertaken in foreign countries, analyzing the forest hazards occurred in entire forests of Korea in the past, and hearing the opinions of people engaged in forestry. 2. Methods of the Study First, reference studies on insurance at large as well as on forest insurance were intensively made to draw the characteristics of forest insurance practiced in main forestry countries, Second, the investigations of forest hazards in Korea for the past ten years were made with the help of the Office of Forestry. Third, the questionnaires concerning forest insurance were prepared and delivered at random to 533 personnel who are working at different administrative offices of forestry, forest stations, forest cooperatives, colleges and universities, research institutes, and fire insurance companies. Fourth, fifty three representative forest owners in the area of three forest types (coniferous, hardwood, and mixed forest), a representative region in Kyonggi Province out of fourteen collective forest development programs in Korea, were directly interviewed with the writer. 3. Results of the Study The rate of response to the questionnaire was 74.40% as shown in the table 3, and the results of the questionaire were as follows: (% in the parenthes shows the rates of response; shortages in amount to 100% were due to the facts of excluding the rates of response of minor respondents). 1) Necessity of forest insurance The respondents expressed their opinions that forest insurance must be undertaken to assure forest financing (5.65%); for receiving the reimbursement of replanting costs in case of damages done (35.87%); and to protect silvicultural investments (46.74%). 2) Law of forest insurance Few respondents showed their views in favor of applying the general insurance regulations to forest insurance practice (9.35%), but the majority of respondents were in favor of passing a special forest insurance law in the light of forest characteristics (88.26%). 3) Sorts of institutes to undertake forest insurance A few respondents believed that insurance companies at large could take care of forest insurance (17.42%); forest owner's mutual associations would manage the forest insurance more effectively (23.53%); but the more than half of the respondents were in favor of establishing public or national forest insurance institutes (56.18%). 4) Kinds of risks to be undertaken in forest insurance It would be desirable that the risks to be undertaken in forest insurance be limited: To forest fire hazards only (23.38%); to forest fire hazards plus damages made by weather (14.32%); to forest fire hazards, weather damages, and insect damages (60.68%). 5) Objectives to be insured It was responded that the objectives to be included in forest insurance should be limited: (1) To artificial coniferous forest only (13.47%); (2) to both coniferous and broad-leaved artificial forests (23.74%); (3) but the more than half of the respondents showed their desire that all the forests regardless of species and the methods of establishment should be insured (61.64%). 6) Range of risks in age of trees to be included in forest insurance The opinions of the respondents showed that it might be enough to insure the trees less than ten years of age (15.23%); but it would be more desirous of taking up forest trees under twenty years of age (32.95%); nevertheless, a large number of respondents were in favor of underwriting all the forest trees less than fourty years of age (46.37%). 7) Term of a forest insurance contract Quite a few respondents favored a contract made on one year basis (31.74%), but the more than half of the respondents favored the contract made on five year bases (58.68%). 8) Limitation in a forest insurance contract The respondents indicated that it would be desirable in a forest insurance contract to exclude forests less than five hectars (20.78%), but more than half of the respondents expressed their opinions that forests above a minimum volume or number of trees per unit area should be included in a forest insurance contract regardless of the area of forest lands (63.77%). 9) Methods of contract Some responded that it would be good to let the forest owners choose their forests in making a forest insurance contract (32.13%); others inclined to think that it would be desirable to include all the forests that owners hold whenerver they decide to make a forest insurance contract (33.48%); the rest responded in favor of forcing the owners to buy insurance policy if they own the forests that were established with subsidy or own highly vauable growing stock (31.92%) 10) Rate of premium The responses were divided into three categories: (1) The rate of primium is to be decided according to the regional degree of risks(27.72%); (2) to be decided by taking consideration both regional degree of risks and insurable values(31.59%); (3) and to be decided according to the rate of risks for the entire country and the insurable values (39.55%). 11) Payment of Premium Although a few respondents wished to make a payment of premium at once for a short term forest insurance contract, and an annual payment for a long term contract (13.80%); the majority of the respondents wished to pay the premium annually regardless of the term of contract, by employing a high rate of premium on a short term contract, but a low rate on a long term contract (83.71%). 12) Institutes in charge of forest insurance business A few respondents showed their desire that forest insurance be taken care of at the government forest administrative offices (18.75%); others at insurance companies (35.76%); but the rest, the largest number of the respondents, favored forest associations in the county. They also wanted to pay a certain rate of premium to the forest associations that issue the insurance (44.22%). 13) Limitation on indemnity for damages done In limitation on indemnity for damages done, the respondents showed a quite different views. Some desired compesation to cover replanting costs when young stands suffered damages and to be paid at the rate of eighty percent to the losses received when matured timber stands suffered damages(29.70%); others desired to receive compensation of the actual total loss valued at present market prices (31.07%); but the rest responded in favor of compensation at the present value figured out by applying a certain rate of prolongation factors to the establishment costs(36.99%). 14) Raising of funds for forest insurance A few respondents hoped to raise the fund for forest insurance by setting aside certain amount of money from the indemnity paid (15.65%); others wished to raise the fund by levying new forest land taxes(33.79%); but the rest expressed their hope to raise the fund by reserving certain amount of money from the surplus money that was saved due to the non-risks (44.81%). 15) Causes of fires The main causes of forest fires 6gured out by the respondents experience turned out to be (1) an accidental fire, (2) cigarettes, (3) shifting cultivation. The reponses were coincided with the forest fire analysis made by the Office of Forestry. 16) Fire prevention The respondents suggested that the most important and practical three kinds of forest fire prevention measures would be (1) providing a fire-break, (2) keeping passers-by out during the drought seasons, (3) enlightenment through mass communication systems. 4. Suggestions The writer wishes to present some suggestions that seemed helpful in drawing up a forest insurance system by reviewing the findings in the questionaire analysis and the results of investigations on forest insurance undertaken in foreign countries. 1) A forest insurance system designed to compensate the loss figured out on the basis of replanting cost when young forest stands suffered damages, and to strengthen credit rating by relieving of risks of damages, must be put in practice as soon as possible with the enactment of a specifically drawn forest insurance law. And the committee of forest insurance should be organized to make a full study of forest insurance system. 2) Two kinds of forest insurance organizations furnishing forest insurance, publicly-owned insurance organizations and privately-owned, are desirable in order to handle forest risks properly. The privately-owned forest insurance organizations should take up forest fire insurance only, and the publicly-owned ought to write insurance for forest fires and insect damages. 3) The privately-owned organizations furnishing forest insurance are desired to take up all the forest stands older than twenty years; whereas, the publicly-owned should sell forest insurance on artificially planted stands younger than twenty years with emphasis on compensating replanting costs of forest stands when they suffer damages. 4) Small forest stands, less than one hectare holding volume or stocked at smaller than standard per unit area are not to be included in a forest insurance writing, and the minimum term of insuring should not be longer than one year in the privately-owned forest insurance organizations although insuring period could be extended more than one year; whereas, consecutive five year term of insurance periods should be set as a mimimum period of insuring forest in the publicly-owned forest insurance organizations. 5) The forest owners should be free in selecting their forests in insuring; whereas, forest owners of the stands that were established with subsidy should be required to insure their forests at publicly-owned forest insurance organizations. 6) Annual insurance premiums for both publicly-owned and privately-owned forest insurance organizations ought to be figured out in proportion to the amount of insurance in accordance with the degree of risks which are grouped into three categories on the basis of the rate of risks throughout the country. 7) Annual premium should be paid at the beginning of forest insurance contract, but reduction must be made if the insuring periods extend longer than a minimum period of forest insurance set by the law. 8) The compensation for damages, the reimbursement, should be figured out on the basis of the ratio between the amount of insurance and insurable value. In the publicly-owned forest insurance system, the standard amount of insurance should be set on the basis of establishment costs in order to prevent over-compensation. 9) Forest insurance business is to be taken care of at the window of insurance com pnies when forest owners buy the privately-owned forest insurance, but the business of writing the publicly-owned forest insurance should be done through the forest cooperatives and certain portions of the premium be reimbursed to the forest cooperatives. 10) Forest insurance funds ought to be reserved by levying a property tax on forest lands. 11) In order to prevent forest damages, the forest owners should be required to report forest hazards immediately to the forest insurance organizations and the latter should bear the responsibility of taking preventive measures.